AD methods only with normal data are widely studied [1]}.
The methods closest to the proposed are the tree-based AD.
Isolation Forest (IF) [2]} grows decision trees randomly and the shallower the tree depth for the data is, the more anomalous the data is.
However, this approach shows performance degradation when feature dimensionality increases.
Robust Random Cut Forest (RRCF, [3]}) further improves IF by choosing features to split based on the size of its range, but it is more sensitive to scale.
PIDForest [4]} zooms on the features which have large variances which makes it robust to noisy or irrelevant features.
